Perfusion MR imaging of brain neoplasms

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas

OBJECTIVES

The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas.

METHODS

Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI. Relative arteriolar cerebral blood volume (rCBVa) and minimum apparent diffusion coefficient (mADC) were compared between low-grade and high-grade gliomas. Receiver operating characteristic (ROC) curve analysis was performed to compare the tumor grading efficiency of rCBVa, mADC, and the combination of the two parameters.

RESULTS

Two observers diagnosed accurate tumor grade in 40 of 51 (78.4%) patients in the first review and in 46 of 51 (90.2%) in the second review. Both rCBVa and mADC showed significant differences between low-grade and high-grade gliomas. ROC analysis gave a threshold value of 1.52 for rCBVa and 0.85 × 10-3 mm2/s for mADC to provide a sensitivity and specificity of 88.0 and 81.2% and 100.0 and 68.7%, respectively. The area under the ROC curve (AUC) was 0.87 and 0.85 for rCBVa and mADC, respectively. The combination of rCBVa and mADC values increased the AUC to 0.92.

CONCLUSION

The combined application of iVASO and DWI may improve the diagnostic accuracy of glioma grading.

Evaluating survival in subjects with astrocytic brain tumors by dynamic susceptibility-weighted perfusion MR imaging

Purpose

Studies have evaluated the application of perfusion MR for predicting survival in patients with astrocytic brain tumors, but few of them statistically adjust their results to reflect the impact of the variability of treatment administered in the patients. Our aim was to analyze the association between the perfusion values and overall survival time, with adjustment for various clinical factors, including initial treatments and follow-up treatments.

Materials and methods

This study consisted of 51 patients with astrocytic brain tumors who underwent perfusion-weighted MRI with MultiHance® at a dose of 0.1 mmol/kg prior to initial surgery. We measured the mean rCBV, the 5% & 10% maximum rCBV, and the variation of rCBV in the tumors. Comparisons were made between patients with and without 2-year survival using two-sample t-test or Wilcoxon rank-sum test for the continuous data, or chi-square and Fisher exact tests for categorical data. The multivariate cox-proportional hazard regression was fit to evaluate the association between rCBV and overall survival time, with adjustment for clinical factors.

Results

Patients who survived less than 2 years after diagnosis had a higher mean and maximum rCBV and a larger variation of rCBV. After adjusting for clinical factors including therapeutic measures, we found no significant association of overall survival time within 2 years with any of these rCBV values.

Conclusions

Although patients who survived less than 2 years had a higher mean and maximum rCBV and a larger variation of rCBV, rCBV itself may not be used independently for predicting 2-year survival of patients with astrocytic brain tumors.

Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. They play a crucial role in providing pre-operative assessment of tumor histology, grading, and biopsy guidance. However, the manual contouring of these neoplasms is tedious, expensive, time-consuming, and vulnerable to inter-observer variability. In this work, we propose a 3D mask region-based convolutional neural network (R-CNN) method to automatically segment brain tumors in DSCE MRI perfusion images. As our goal is to simultaneously localize and segment the tumor, our training process contained both a region-of-interest (ROI) localization and regression with voxel-wise segmentation. The combination of classification loss, ROI location and size regression loss, and segmentation loss were used to supervise the proposed network. We retrospectively investigated 21 patients' perfusion images, with between 50 and 70 perfusion time point volumes, a total of 1260 3D volumes. Tumor contours were automatically segmented by our proposed method and compared against other state-of-the-art methods and those delineated by physicians as the ground truth. The results of our method demonstrated good agreement with the ground truth contours. The average DSC, precision, recall, Hausdorff distance, mean surface distance (MSD), root MSD, and center of mass distance were 0.90 ± 0.04, 0.91 ± 0.04, 0.90 ± 0.06, 7.16 ± 5.78 mm, 0.45 ± 0.34 mm, 1.03 ± 0.72 mm, and 0.86 ± 0.91 mm, respectively. These results support the feasibility of our method in accurately localizing and segmenting brain tumors in DSCE perfusion MRI. Our 3D Mask R-CNN segmentation method in DSCE perfusion imaging has great promise for future clinical use.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

Objective

To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup.

Materials and Methods

Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade.

Results

The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68).

Conclusion

Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.

The combined role of MR spectroscopy and perfusion imaging in preoperative differentiation between high- and low-grade gliomas

Background

Brain tumors are an important health problem. The preoperative classification of gliomas by non-invasive techniques is a significant problem. Relative cerebral blood volume and spectroscopy have the ability to sample the entire lesion non-invasively. The present study aims to evaluate the combined role of dynamic susceptibility perfusion and spectroscopy in the classification of primary brain tumors. The combination of both provides overall diagnostic accuracy (100%). Relative cerebral blood volume in peritumoral region plays an important additional role in this regard.

Results

On the basis of histopathology, among 50 patients with brain tumors, high-grade gliomas accounted for 58%, while low-grade gliomas accounted for 42%. The relative cerebral blood volume in the tumor had the best sensitivity, specificity, and accuracy of 96.8%, 95.3%, and 96, respectively. The use of relative cerebral blood volume and choline/N-acetyl Aspartate increased diagnostic accuracy by 100%.

Conclusion

The combination of magnetic resonance spectroscopy and perfusion can increase sensitivity and positive predictive value to define the degree of glioma.

Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT

The purpose of this study was to investigate the value of the cerebral blood volume (CBV) obtained with perfusion computed tomography (CT) and the electron density (ED) measured by dual-energy CT for differentiating high- from low-grade glioma (HGG, LGG).The CBV and ED were obtained in 9 LGG and 7 HGG patients. The CBV and ED of LGGs and HGGs were compared. Receiver operating characteristic (ROC) curves were generated for CBV, ED, and CBV plus ED. The correlation between CBV, ED, and the MIB-1 labeling index of the tumors was examined. All of these analyses were also performed using relative CBV (rCBV) and ED (rED) (the value of tumors/the value of contralateral white matter).The mean CBV, ED, rCBV, and rED values were significantly higher in HGG than LGG (P < .05). By ROC analysis, the combination of rCBV plus rED as well as CBV plus ED were more accurate than CBV, ED, rCBV, rED alone. There was a significant correlation between ED and MIB-1 (P = .04).ED improved diagnostic accuracy of perfusion CT for differentiating HGG from LGG.

Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images

Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.

Comparison between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labeling techniques in distinguishing malignant from benign brain tumors

OBJECTIVES

The purpose of this study was to preliminarily compare unenhanced arterial spin-labeled (ASL) imaging, dynamic susceptibility contrast-enhanced cerebral blood volume (DSCE-CBV) magnetic resonance imaging (MRI) for evaluation of tumor perfusion in patients with brain tumors.

MATERIALS AND METHODS

A total of 27 patients with brain tumors were examined in 1,5T MRI. Single phase and multiphase ASL, DSCE-CBV examinations were assessed by both qualitative and quantitative analysis for the detection of malignancy. Imaging results were correlated with a histopathology or follow-up.

RESULTS

Based on 31 studies in 27 patients with brain tumors, the visual inspection sensitivities for ASL and dynamic DSC perfusion imaging were 88% and 94%, respectively, with 100% specificity for both. On qualitative evaluation, sensitivities for ASL and DSC perfusion imaging perfusions were 88% and 94%, respectively, with 100% specificity for both. The highest sensitivity values for quantitative ASL imaging were obtained using a normalized cut-off ratio of 1.65, resulting in sensitivity of 94% for ASL imaging and cut-off ratio of 1.95 and sensitivity 94% for DSCE-CBV imaging.

CONCLUSION

The present study revealed similar sensitivity and specificity for both multhiphase ASL and DSC MRI. Thus, we suggest that ASL perfusion can be used in daily clinical practice.

Arterial input function and gray matter cerebral blood volume measurements in children

Purpose

To investigate how arterial input functions (AIFs) vary with age in children and compare the use of individual and population AIFs for calculating gray matter CBV values. Quantitative measures of cerebral blood volume (CBV) using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) require measurement of an AIF. AIFs are affected by numerous factors including patient age. Few data presenting AIFs in the pediatric population exists.

Materials and Methods

Twenty‐two previously treated pediatric brain tumor patients (mean age, 6.3 years; range, 2.0–15.3 years) underwent DSC‐MRI scans on a 3T MRI scanner over 36 visits. AIFs were measured in the middle cerebral artery. A functional form of an adult population AIF was fitted to each AIF to obtain parameters reflecting AIF shape. The relationship between parameters and age was assessed. Correlations between gray matter CBV values calculated using the resulting population and individual patient AIFs were explored.

Results

There was a large variation in individual patient AIFs but correlations between AIF shape and age were observed. The center (r = 0.596, P < 0.001) and width of the first‐pass peak (r = 0.441, P = 0.007) were found to correlate significantly with age. Intrapatient coefficients of variation were significantly lower than interpatient values for all parameters (P < 0.001). Differences in CBV values calculated with an overall population and age‐specific population AIF compared to those calculated with individual AIFs were 31.3% and 31.0%, respectively.

Conclusion

Parameters describing AIF shape correlate with patient age in line with expected changes in cardiac output. In pediatric DSC‐MRI studies individual patient AIFs are recommended. J. Magn. Reson. Imaging 2016;43:981–989

Glioma Angiogenesis and Perfusion Imaging: Understanding the Relationship between Tumor Blood Volume and Leakiness with Increasing Glioma Grade

BACKGROUND AND PURPOSE

The purpose of this study was to investigate imaging correlates to the changes occurring during angiogenesis in gliomas. This was accomplished through in vivo assessment of vascular parameters (relative CBV and permeability surface-area product) and their changing relationship with increasing glioma grade.

MATERIALS AND METHODS

Seventy-six patients with gliomas underwent preoperative perfusion CT and assessment of relative CBV and permeability surface-area product. Regression analyses were performed to assess the rate of change between relative CBV and permeability surface-area product and to test whether these differed for distinct glioma grades. The ratio of relative CBV to permeability surface-area product was also computed and compared among glioma grades by using analysis of variance methods.

RESULTS

The rate of change in relative CBV with respect to permeability surface-area product was highest for grade II gliomas followed by grade III and then grade IV (1.64 versus 0.91 versus 0.27, respectively). The difference in the rate of change was significant between grade III and IV (P = .003) and showed a trend for grades II and IV (P = .098). Relative CBV/permeability surface-area product ratios were the highest for grade II and lowest for grade IV. The pair-wise difference among all 3 groups was significant (P < .001).

CONCLUSIONS

There is an increase in relative CBV more than permeability surface-area product in lower grade gliomas, whereas in grade III and especially grade IV gliomas, permeability surface-area product increases much more than relative CBV. The rate of change of relative CBV with respect to permeability surface-area product and relative CBV/permeability surface-area product ratio can serve as an imaging correlate to changes occurring at the tumor microvasculature level.

Outcome of radiosurgery for recurrent malignant gliomas: assessment of treatment response using relative cerebral blood volume

Gamma knife radiosurgery (GKS) is efficacious for treating recurrent malignant gliomas as a salvage treatment. However, contrast enhancement alone on MR imaging remains difficult to determine the treatment response following GKS. The purpose of this study was to evaluate the radiosurgical effect for recurrent malignant gliomas and to clarify if relative cerebral blood volume (rCBV) derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging could represent the treatment response. Between March 2006 and December 2008, 38 patients underwent GKS for recurrent malignant gliomas. Before and after GKS, DSC perfusion MR imaging datasets were retrospectively reprocessed and regions of interest were drawn around the contrast-enhancing region targeted with GKS. DSC-perfusion MR scans were assessed at a regular interval of two months. Following GKS for the recurrent lesions, MR images showed response (stable disease or partial response) in 26 of 38 patients (68.4 %) at post-GKS 2 months and 18 of 38 patients (47.3 %) at post-GKS 4 months. Initial mean rCBV value was 2.552 (0.586-6.178) at the pre-GKS MRI. In the response group, mean rCBV value was significantly decreased (P < 0.05) at the follow up of 2 and 4 months. However, in the treatment-failure group, mean rCBV value had no significant change. We suggest that GKS is an alternative treatment choice for the recurrent glioma. DSC-perfusion MR images are helpful to predict the treatment response after GKS.

Definition of K(trans) and FA thresholds for better assessment of experimental glioma using high-field MRI: a feasibility study

PURPOSE

To define K(trans) and fractional anisotropy (FA) thresholds in correlation to histology for improved magnetic resonance imaging (MRI) tumor assessment in an animal model of brain glioma.

METHODS

Twelve rats underwent 4.7 T MRI at day 10 after tumor implantation. Anatomical scans (T2, T1 at 8 min after double dose contrast application) as well as dynamic contrast-enhanced (DCE) imaging with calculation of K(trans) and diffusion tensor imaging (DTI) with calculation of FA were performed. T2- and T1-derived tumor volumes were calculated and thresholds for K(trans) and FA were defined for best MRI tumor assessment correlated to histology.

RESULTS

Tumor volumes were 159 ± 14 mm(3) (histology), 126 ± 26 mm(3) (T1 with contrast, r=0.76), and 153 ± 12 mm(3) (T2, r=0.84), respectively. K(trans)- and FA-derived tumor volumes were 160 ± 16 mm(3) (for K(trans ≥ 0.04 min(-1), r=0.94), and 159 ± 14 mm(3) (for FA £0.14, r=0.96), respectively.

CONCLUSIONS

DCE-MRI and DTI with calculation of K(trans) and FA maps allow very precise brain glioma assessment comparable to histology if established thresholds for the given tumor model are used.

The role of dynamic susceptibility contrast-enhanced perfusion MR imaging in differentiating between infectious and neoplastic focal brain lesions: results from a cohort of 100 consecutive patients

Background and Purpose

Differentiating between infectious and neoplastic focal brain lesions that are detected by conventional structural magnetic resonance imaging (MRI) may be a challenge in routine practice. Brain perfusion-weighted MRI (PWI) may be employed as a complementary non-invasive tool, providing relevant data on hemodynamic parameters, such as the degree of angiogenesis of lesions. We aimed to employ dynamic susceptibility contrast-enhanced perfusion MR imaging (DSC-MRI) to differentiate between infectious and neoplastic brain lesions by investigating brain microcirculation changes.

Materials and Methods

DSC-MRI perfusion studies of one hundred consecutive patients with non-cortical neoplastic (n = 54) and infectious (n = 46) lesions were retrospectively assessed. MRI examinations were performed using a 1.5-T scanner. A preload of paramagnetic contrast agent (gadolinium) was administered 30 seconds before acquisition of dynamic images, followed by a standard dose 10 seconds after starting imaging acquisitions. The relative cerebral blood volume (rCBV) values were determined by calculating the regional cerebral blood volume in the solid areas of lesions, normalized to that of the contralateral normal-appearing white matter. Discriminant analyses were performed to determine the cutoff point of rCBV values that would allow the differentiation of neoplastic from infectious lesions and to assess the corresponding diagnostic performance of rCBV when using this cutoff value.

Results

Neoplastic lesions had higher rCBV values (4.28±2.11) than infectious lesions (0.63±0.49) (p<0.001). When using an rCBV value <1.3 as the parameter to define infectious lesions, the sensitivity of the method was 97.8% and the specificity was 92.6%, with a positive predictive value of 91.8%, a negative predictive value of 98.0%, and an accuracy of 95.0%.

Conclusion

PWI is a useful complementary tool in distinguishing between infectious and neoplastic brain lesions; an elevated discriminatory value for diagnosis of infectious brain lesions was observed in this sample of patients when the rCBV cutoff value was set to 1.3.

Quantitative MR perfusion parameters related to survival time in high-grade gliomas

OBJECTIVES

To evaluate the quantitative parameters obtained from dynamic MR T2*-weighted images as predictors of survival taking into consideration the biasing effects of other survival-related covariates.

METHODS

Thirty-nine patients (60 ± 14 years; survival 267 ± 191 days) with high-grade gliomas (8 grade III, 31 grade IV) were retrospectively included in the study. Additional data incorporated Karnofsky performance scale, tumour resection extension after surgery and type of treatment. Dynamic T2*-weighted MRI was acquired before treatment. Tumour curves were extracted for each voxel, and several quantitative parameters were obtained from the whole tumour volume and the 10 % maximum values. Additional image covariates included the presence of necrosis, single or multiple lesions, and tumour and oedema volumes. The relationship between quantitative parameters and survival was assessed using clusterisation techniques and the log-rank method. Cox regression analysis was used to evaluate each parameter's predictive value.

RESULTS

Only the mean of the 10 % maximum values of the transfer coefficient showed an independent relationship with patient survival (log-rank chi-squared test <0.001, Cox regression P = 0.015), with higher values corresponding to lower survival rates.

CONCLUSIONS

High maximum transfer coefficient values show an independent statistical relationship with low survival in high-grade glioma patients. This imaging biomarker can be used as a predictor of prognosis.

Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study

OBJECTIVE

Differentiation of grade 3 astrocytoma from glioblastoma multiforme can be difficult with conventional structural imaging but is important for prognosis. The purpose of this study was to assess perfusion CT in differentiating high-grade gliomas (HGGs) and their role in prognosis in the care of patients with HGG.

SUBJECTS AND METHODS

Twenty patients with previously untreated HGG underwent prospective evaluation with perfusion CT. Permeability surface area product (PS) and cerebral blood volume (CBV) were calculated by the deconvolution method and were compared between HGGs with Student two-sample t tests. Receiver operating characteristic curves were generated for PS, CBV, and the conjoint factor PS + CBV. Cox regression analysis was used to correlate these parameters with patient survival over a follow-up period. Hazard ratios were calculated, and Kaplan-Meier survival curves were drawn.

RESULTS

There was a significant difference between grade 3 and grade 4 gliomas for PS (p = 0.022) and PS + CBV (p = 0.019) but not for CBV alone (p = 0.411). Receiver operating characteristic analyses showed that PS (area under the curve [AUC], 0.72) and CBV + PS (AUC, 0.73) can be used to differentiate grade 3 from grade 4 gliomas but that CBV alone cannot be so used (AUC, 0.54). There was a significant relation between patient outcome and age (p = 0.034) and CBV + PS (p = 0.048). Patients with HGG and a CBV + PS greater than 9 had a poor outcome (hazard ratio, 6.00).

CONCLUSION

PS and CBV + PS can be used to differentiate grade 3 from grade 4 gliomas. The outcome of patients with HGG depends on age and CBV + PS.

Bevacizumab-induced oral mucositis in background of cutaneous plaque-type psoriasis

OBJECTIVE

To report the serial development of oral mucositis following infusion of bevacizumab in a young woman with a malignant brain tumor and history of cutaneous psoriasis.

CASE SUMMARY

A 29-year-old woman with a history of active cutaneous psoriasis and a malignant glioneuronal tumor was treated with bevacizumab for 2.5 years. With each infusion of bevacizumab, she developed oral mucositis within 36 hours. She received temozolomide as part of concurrent therapy with radiation and as maintenance therapy; it was discontinued after continuous therapy for 1.5 years. Bevacizumab 10 mg/kg was added after 7 cycles of maintenance temozolomide, as the tumor had minimal response and evidence of increased perfusion with angiogenesis on imaging studies. All medication, including temozolomide, was evaluated and eventually discontinued, with the exception of bevacizumab, which remained the drug suspected of causing the mucositis.

DISCUSSION

Oral mucositis is a frequent adverse effect of cytotoxic chemotherapy, but has not been reported with bevacizumab. The Naranjo probability scale indicated a probable adverse drug reaction. This likely indicates that bevacizumab is one of many drugs known to induce exacerbation of psoriatic disease. We speculate that oral mucositis developed as bevacizumab-induced generation of proinflammatory cytokines within the vascular endothelium, leading to mucosal damage and ulceration. In addition, interruption of reparative angiogenic pathways with bevacizumab likely contributed to the severity of mucositis.

CONCLUSIONS

Clinicians should be aware that bevacizumab can potentially exacerbate psoriatic disease.

Analysis of the layering pattern of the apparent diffusion coefficient (ADC) for differentiation of radiation necrosis from tumour progression

OBJECTIVES

To evaluate the added value of diffusion-weighted imaging (DWI) to perfusion-weighted imaging (PWI) for differentiating tumour progression from radiation necrosis.

METHODS

Sixteen consecutive patients who underwent removal of a metastatic brain tumour that increased in size after stereotactic radiosurgery were retrospectively reviewed. The layering of the ADC was categorised into three patterns. ADC values were measured on each layer, and the maximum rCBV was measured. rCBV and the layering pattern of the ADC of radiation necrosis and tumour progression were compared.

RESULTS

Nine cases of radiation necrosis and seven cases of tumour progression were pathologically confirmed. Radiation necrosis (88.9 % vs. 14.3 %) showed a three-layer pattern of ADC with a middle layer of minimum ADC more frequently. If rCBV larger than 2.6 was used to differentiate radiation necrosis and tumour progression, the sensitivity was 100 % but specificity was 56 %. If the lesions with the three-layer pattern of ADC with moderately increased rCBV (2.6-4.1) were excluded from tumour progression, the sensitivity and specificity increased to 100 %.

CONCLUSIONS

The three-layer pattern of ADC shows high specificity in diagnosing radiation necrosis; therefore, combined analysis of the ADC pattern with rCBV may have added value in the correct differentiation of tumour progression from radiation necrosis.

The Role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard

BACKGROUND AND PURPOSE

Contrast extravasation in DSC-MRI potentiates inaccurate and imprecise estimates of glioma rCBV. We tested assertions that preload and postprocessing algorithms minimize this error by comparing Gd-rCBV using permutations of these 2 techniques with criterion standard rCBV using MION, an intravascular agent.

MATERIALS AND METHODS

We imaged 7 Fisher rats with 9L gliosarcomas, by using 3T gradient-echo DSC-MRI with MION (2.0 mg Fe/kg) and staged injection of Gd-diethylene triamine pentaacetic acid: a 0.1-mmol/kg bolus provided no preload (P-) data and served as preload (P+) for a subsequent 0.2-mmol/kg bolus. We computed MION-rCBV (steady-state ΔR2*, tumor versus normal brain) and Gd-rCBV ΔR2* [t] integration) without (C-) and with (C+) postprocessing correction, thereby testing 4 correction permutations: P-C-, P-C+, P+C-, and P+C+. We tested whether each permutation reduced bias and variance of the Gd/MION rCBV differences by using generalized estimating equations and Fmax statistics (P < .05 significant).

RESULTS

Gd-rCBV progressively better approximated MION-rCBV with increasing leakage correction. There was no statistically significant bias for the mean percentage deviation of Gd-rCBV from MION-rCBV for any correction permutation, but there was significantly reduced variance by using P+C- (22-fold), P-C+ (32-fold), and P+C+ (267-fold) compared with P-C-. P+C+ provided significant additional variance reduction compared with P+C- (12-fold) and P-C+ (8-fold). Linear regression of Gd-rCBV versus MION-rCBV revealed P+C+ to have the closest slope and intercept compared with the ideal, substantially better than P+C-.

CONCLUSIONS

Preload and postprocessing correction significantly reduced the variance of Gd-rCBV estimates, and bias reduction approached significance. Postprocessing correction provide significant benefit beyond preload alone.

Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases

PURPOSE

To assess the performance of parameters used in conventional magnetic resonance imaging (MRI), perfusion-weighted MR imaging (PWI) and visual texture analysis, alone and in combination, to differentiate a single brain metastasis (MET) from glioblastoma multiforme (GBM).

PATIENTS AND METHODS

In a retrospective study of 50 patients (41 GBM and 14 MET) who underwent T2/FLAIR/T1(post-contrast) imaging and PWI, morphological (circularity, surface area), perfusion (rCBV in the ring-like tumor area, rCBV in the peritumoral area, percentage of signal intensity recovery at the end of first pass) and texture parameters in the peritumoral area were estimated. Statistical differences and performances were assessed using Wilcoxon's test and receiver operating characteristic curves, respectively. Multiparametric classification of tumors was performed using k-means clustering.

RESULTS

Significant statistical differences in circularity, surface area, rCBVs, percentage of signal intensity recovery and texture parameters (energy, entropy, homogeneity, correlation, inverse differential moment, sum average) were observed between MET and GBM (P<0.05). Moderate-to-good classification performances were found with these parameters. Clustering based on rCBV and texture parameters (contrast, sum average) differentiated MET from GBM with a sensitivity of 92% and a specificity of 71%.

CONCLUSION

Combining perfusion and visual texture parameters within a statistical classifier significantly improved the differentiation of a single brain MET and GBM.

Mean intensity curve on dynamic contrast-enhanced susceptibility-weighted perfusion MR imaging--review of a new parameter to differentiate intracranial tumors

Dynamic susceptibility contrast (DSC) perfusion imaging has been in clinical use for various indications, including characterization and grading of intracranial neoplasms. However, several technical factors can lead to pitfalls in image interpretation. This review discusses the extraction of T1 and T2* information from mean curve analysis of DSC perfusion imaging of various brain tumors, which provides further insights into tumor biology and, thus, may be useful in the differential diagnosis of such tumors. Indeed, by looking at the mean time-signal intensity curve from the tumor bed in addition to the rCBV maps, it is possible to obtain further inferences of capillary density and lesion leakiness. When dynamic contrast enhanced (DCE) T1 perfusion is not available, DSC perfusion with mean curve analysis appears to be a valid alternative for characterizing various brain neoplasms in a routine clinical setting.

Dynamic susceptibility contrast-enhanced MRI evaluation of cerebral intraventricular tumors: preliminary results

INTRODUCTION

The aims of the present study were to determine the perfusion characteristics of several types of intraventricular tumors and to evaluate the usefulness of dynamic contrast-enhanced MRI in making the differential diagnosis.

METHODS

A total of 28 patients with intraventricular tumors (five meningiomas, five papillomas, three ependymomas, four subependymomas, seven central neurocytomas, two subependymal giant cell astrocytomas and two metastases) underwent conventional and dynamic susceptibility contrast-enhanced MRI. Cerebral blood volume (CBV) maps were obtained and the relative CBV (rCBV) calculated for each tumor. Mean rCBV(max) values were compared across the different types of tumors (ANOVA, P=0.05).

RESULTS

Intraventricular tumors presented with three different patterns of vascularization: highly vascularized tumors (mean rCBV(max)>3), including papillomas, meningiomas and renal carcinoma metastases; poorly vascularized tumors (mean rCBV(max)<2), including ependymomas and subependymomas; and intermediately vascularized tumors (mean rCBV(max)>2 but<3), including central neurocytomas and lung metastases. There was a significant difference between the highly vascularized (papillomas, meningiomas) and poorly vascularized (subependymomas) tumors. In cases of suspected meningioma, papilloma or neurocytoma, low rCBV values (<3) point to a diagnosis of neurocytoma rather than either of the other tumor types.

CONCLUSION

Susceptibility contrast-enhanced MRI can provide additional information on the vascularization of intraventricular cerebral tumors and may help in making the differential diagnosis.

Role of perfusion magnetic resonance imaging in cervical lymphadenopathy

PURPOSE

To assess the role of perfusion magnetic resonance (MR) imaging in patients with cervical lymphadenopathy.

MATERIALS AND METHODS

Dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging was performed on 45 cervical lymph nodes after a bolus injection of gadolinium-DTPA (0.1 mmol/kg body weight). Time signal intensity curve was created and dynamic susceptibility contrast (DSC) percentage of the lymph nodes was calculated. Receiver operating characteristic curve analysis was used to investigate whether DSC percentage could aid in the characterization of cervical lymphadenopathy.

RESULTS

The mean (SD) DSC percentage of malignant nodes (44.8% [6.2%]) was significantly different (P = 0.001) from that of benign nodes (28.8% [4.8%]). The mean (SD) DSC percentage of metastatic nodes (48.72% [2.4%]) was significantly different (P = 0.001) than that of lymphoma (37.09% [3.5%]). The DSC percentage threshold value used for differentiating malignant from benign nodes and metastatic from lymphomatous nodes were 34.3% and 43.5%, with areas under the curve of 0.95 and 0.97, respectively.

CONCLUSIONS

Perfusion MR imaging is a noninvasive promising method that can be used for differentiation of malignant from benign cervical lymph nodes, and it helps in the characterization of malignant cervical lymphadenopathy.

Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas

PURPOSE

To evaluate if the relative tumor blood volume (rTBV) using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) can aid in distinguishing low- from high-grade nonenhancing astrocytomas.

MATERIALS AND METHODS

Seventeen patients with histologically proven astrocytomas underwent MRI including DSC-MRI. Maximum TBV regions of interest were recorded from each neoplasm and normalized to contralateral normal white matter. Demographic features, diagnostic MRI findings including tumor volumes, and the normalized rTBV ratios were compared between low-grade (I and II, LGA, n = 6) and high-grade (III) astrocytomas (HGA, n = 11) using Mann-Whitney's U-test and receiver operating characteristic (ROC) analysis.

RESULTS

Maximum rTBV ratios were statistically higher for HGA (1.11 ± 0.13) than LGA (0.66 ± 0.17, P < 0.005) with the best cutoff threshold at 0.94 (sensitivity of 90.9%, specificity of 100%). Differences in mean age and tumor volume on fluid-attenuated inversion recovery (FLAIR) imaging between the two groups did not reach statistical difference (P = 0.22, 0.36).

CONCLUSION

The addition of DSC-MRI can aid in accurate grading of nonenhancing astrocytomas with high sensitivity and specificity.

MR imaging of late radiation therapy- and chemotherapy-induced injury: a pictorial essay

Radiation to the brain and adjuvant chemotherapy may produce late delayed changes from several months to years after treatment of intracranial malignancies with a reported prevalence of 5-24%. The pattern of treatment-related injury may vary from diffuse periventricular white matter lesions to focal or multifocal lesions. Differentiation of treatment-related injury from tumor progression/recurrence may be difficult with conventional MR imaging (MRI). With both disease processes, the characteristic but nonspecific imaging features are vasogenic edema, contrast enhancement, and mass effect. This pictorial essay presents MRI spectra of late therapy-induced injuries in the brain with a particular emphasis on radiation necrosis, the most common and severe form. Novel MRI techniques, such as diffusion-weighted imaging (DWI), proton MR spectroscopy (MRS), and perfusion MRI, improve the possibilities of better characterization of treatment-related changes. Advanced MRI techniques allow for the assessment of metabolism and physiology and may increase specificity for therapy-induced changes.

Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging

PURPOSE

The purpose of our study was to determine the statistical significance of thresholds of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and ADC ratios in grading cerebral gliomas.

MATERIALS AND METHODS

In this retrospective study, 51 patients with histopathologically confirmed primary cerebral gliomas who had undergone conventional MR imaging, dynamic contrast-enhanced T2*-weighted perfusion MR imaging and diffusion MR imaging were included. A retrospective blinded analysis of the imaging findings including the perfusion and diffusion parameters was done. The rCBV measurements were obtained from regions of maximum perfusion. Minimum ADC values were obtained from the region of maximum hypointensity within the tumor and from the corresponding opposite white matter. Tumor grade determined with the two methods were then compared with the histopathologic grade. Mann-Whitney tests were performed to compare the DWI and PWI between tumor types. Receiver operating characteristic analyses were performed to determine optimum thresholds for tumor grading and also to calculate the sensitivity, specificity, PPV, and NPV for identifying high-grade gliomas.

RESULTS

Statistical analysis demonstrated a threshold value of 2.91 for rCBV to provide sensitivity, specificity, PPV, and NPV of 94.7, 93.75, 90.0, and 96.8%, respectively, in determining high-grade gliomas. An ADC value of 98.50 mm(2)/s was defined as a threshold below which tumors were classified as high-grade gliomas and a sensitivity, specificity, PPV, and NPV of 90, 87.1, 81.81 and 93.10% respectively, were obtained. Significant differences were noted in the rCBV ratios, ADC and ADC ratios between low- and high-grade gliomas (P < 0.0001).

CONCLUSION

Combining PWI and DWI with conventional MR imaging increases the accuracy of pre-operative imaging grading of glial neoplasms. The rCBV measurements had the most superior diagnostic performance in predicting glioma grade. Absolute ADC values or ADC ratios were also helpful in preoperative grading of gliomas. Threshold values can be used in a clinical setting to evaluate tumors preoperatively for histologic grade and provide a means for guiding treatment and predicting postoperative patient outcome.

MR Perfusion Imaging of Intracranial Tumors

This retrospective study evaluated magnetic resonance (MR) perfusion imaging in the study of intracranial tumors; 218 patients were studied with 509 MR examinations. The first aim was to establish the usefulness of perfusion imaging for the differential diagnosis between neoplastic tissue and other lesions both in the first MR examination and in the post therapeutic controls (differentiation of tumor recurrence from radionecrosis). Then we evaluated the presence of infiltrating neoplastic tissue in CBV maps outside the enhancement area to differentiate infiltrating gliomas from metastases. In addition, post surgical evaluation was performed to identify residual neoplastic tissue, mainly if bleeding or inflammatory post surgical cerebral damage did not allow a definitive diagnosis with conventional morphologic images. Lastly, it was assessed whether hypervascularization (evaluated in CBV maps) is a favourable prognostic factor for a positive outcome to radiotherapy. The diagnostic gain of perfusion imaging in all these cases was established evaluating our case records.

Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study

BACKGROUND AND PURPOSE

Although the prognostic value of perfusion MR imaging in various gliomas has been investigated, that in high-grade astrocytomas alone has not been fully evaluated. The purpose of this study was to evaluate retrospectively whether the tumor maximum relative cerebral blood volume (rCBV) on pretreatment perfusion MR imaging is of prognostic value in patients with high-grade astrocytoma.

MATERIALS AND METHODS

Between January 1999 and December 2002, 49 patients (30 men, 19 women; age range, 23-76 years) with supratentorial high-grade astrocytoma underwent MR imaging before the inception of treatment. The patient age, sex, symptom duration, neurologic function, mental status, Karnofsky Performance Scale, extent of surgery, histopathologic diagnosis, tumor component enhancement, and maximum rCBV were assessed to identify factors affecting survival. Kaplan-Meier survival curves, the logrank test, and the multivariate Cox proportional hazards model were used to evaluate prognostic factors.

RESULTS

The maximum rCBV was significantly higher in the 31 patients with glioblastoma multiforme than in the 18 with anaplastic astrocytoma (P < .03). The 2-year overall survival rate was 67% for 27 patients with a low (< or =2.3) and 9% for 22 patients with a high (>2.3) maximum rCBV value (P < .001). Independent important prognostic factors were the histologic diagnosis (hazard ratio = 9.707; 95% confidence interval (CI), 3.163-29.788), maximum rCBV (4.739; 95% CI, 1.950-11.518), extent of surgery (2.692; 95% CI, 1.196-6.061), and sex (2.632; 95% CI, 1.153-6.010).

CONCLUSION

The maximum rCBV at pretreatment perfusion MR imaging is a useful clinical prognostic biomarker for survival in patients with high-grade astrocytoma.

Improving lesion detection and visualization: implications for neurosurgical planning and follow-up

Contrast-enhanced magnetic resonance (MR) imaging is considered the most sensitive method for detecting tumors in the central nervous system (CNS). The primary objective is to improve lesion detection, delineation, and characterization (benign or malignant) in order to more accurately define the location, extent, and type of disease and the appropriate treatment option for improved patient outcome (surgical intervention, radiation therapy or cytotoxic chemotherapy). This article reviews the various types of tumor occurring in the brain and the specific role of contrast-enhanced MR imaging for the evaluation of these tumors. Emphasis is placed on the value of contrast-enhanced MR imaging in the evaluation of primary intra-axial brain lesions and how high relaxivity contrast agents such as MultiHance (Bracco Imaging, Milan, Italy) might improve detection, treatment planning, and follow-up.

Role of perfusion CT in glioma grading and comparison with conventional MR imaging features

BACKGROUND AND PURPOSE

Perfusion imaging using CT can provide additional information about tumor vascularity and angiogenesis for characterizing gliomas. The purpose of our study was to demonstrate the usefulness of various perfusion CT (PCT) parameters in assessing the grade of treatment-naïve gliomas and also to compare it with conventional MR imaging features.

MATERIALS AND METHODS

PCT was performed in 19 patients with glioma (14 high-grade gliomas and 5 low-grade gliomas). Normalized ratios of the PCT parameters (normalized cerebral blood volume [nCBV], normalized cerebral blood flow [nCBF], normalized mean transit time [nMTT]) were used for final analysis. Conventional MR imaging features of these tumors were assessed separately and compared with PCT parameters. Low- and high-grade gliomas were compared by using the nonparametric Wilcoxon 2-sample tests.

RESULTS

Mean nCBV in the high- and low-grade gliomas was 3.06 +/- 1.35 and 1.44 +/- 0.42, respectively, with a statistically significant difference between the 2 groups (P = .005). Mean nCBF for the high- and low-grade gliomas was 3.03 +/- 2.16 and 1.16 +/- 0.36, respectively, with a statistically significant difference between the 2 groups (P = .045). Cut points of >1.92 for nCBV (85.7% sensitivity and 100% specificity), >1.48 for nCBF (71.4% sensitivity and 100% specificity), and <1.94 for nMTT (92.9% sensitivity and 40% specificity) were found to identify the high-grade gliomas. nCBV was the single best parameter; however, using either nCBV of >1.92 or nCBF of >1.48 improved the sensitivity and specificity to 92.9% and 100%, respectively. The sensitivity and specificity for diagnosing a high-grade glioma with conventional MR imaging were 85.7% and 60%, respectively.

CONCLUSIONS

PCT can be used for preoperative grading of gliomas and can provide valuable complementary information about tumor hemodynamics, not available with conventional imaging techniques. nCBV was the single best parameter correlating with glioma grades, though using nCBF when nCBV was <1.92 improved the sensitivity. An nCBV threshold of >1.92 was found to identify the high-grade gliomas.

A prospective study on the added value of pulsed arterial spin-labeling and apparent diffusion coefficients in the grading of gliomas

BACKGROUND AND PURPOSE

The purpose of this study was to determine whether qualitative and quantitative measures obtained with pulsed arterial spin-labeling (PASL) and apparent diffusion coefficients (ADC) improve glioma grading compared with conventional MR images.

MATERIALS AND METHODS

We prospectively performed 2 qualitative consensus reviews in 33 suspected gliomas: 1) conventional MR images alone and 2) conventional MR images with PASL and ADC. To calculate the diagnostic performance parameters of PASL and ADC, we used a qualitative scoring system on the basis of the tumor perfusion signal intensity (sTP) and visual ADC scoring (sADC). We then analyzed quantitative regions of interest and calculated the ratio of the maximum tumor perfusion signal intensity (rTPmax) and the minimum ADC value (mADC).

RESULTS

Two observers diagnosed accurate tumor grades in 23 of 33 (70%) lesions in the first review and in 29 of 33 (88%) lesions in the second review. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for determining a glioma grading by using combined sTP and sADC scoring were 90.9, 90.9, 95.2, and 83.3%, respectively. Statistical analysis gave a threshold value of 1.24 for rTPmax and 0.98 x 10(-3) mm/s(2) for mADC to provide a sensitivity, specificity, PPV, and NPV of 95.5, 81.8, 91.3, and 90.1% and 90.9, 81.8, 90.9, and 81.8%, respectively. The receiver operator characteristic curve analyses showed no significant difference between the quantitative and combined qualitative parameters.

CONCLUSION

PASL and ADC significantly improve the diagnostic accuracy of glioma grading compared with conventional imaging.

Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging

PURPOSE

To investigate the contribution of perfusion-weighted MR imaging (PWI) by using the relative cerebral blood volume (rCBV) ratio in the differential diagnosis of various intracranial space-occupying lesions.

MATERIALS AND METHODS

This study involved 105 patients with lesions (high-grade glioma (N=26), low-grade glioma (N=11), meningioma (N=23), metastasis (N=25), hemangioblastoma (N=6), pyogenic abscess (N=4), schwannoma (N=5), and lymphoma (N=5)). The patients were examined with a T2*-weighted (T2*W) gradient-echo single-shot EPI sequence. The rCBV ratios of the lesions were obtained by dividing the values obtained from the normal white matter. Statistical analysis was performed with the Mann-Whitney U-test. A P-value less than 0.05 was considered statistically significant.

RESULTS

The rCBV ratio was 5.76+/-3.35 in high-grade gliomas, 1.69+/-0.51 in low-grade gliomas, 8.02+/-3.89 in meningiomas, 5.27+/-3.22 in metastases, 11.36+/-4.41 in hemangioblastomas, 0.76+/-0.12 in abscesses, 1.10+/-0.32 in lymphomas, and 3.23+/-0.81 in schwannomas. The rCBV ratios were used to discriminate between 1) high- and low-grade gliomas (P<0.001), 2) hemangioblastomas and metastases (P<0.05), 3) abscesses from high-grade gliomas and metastases (P<0.001), 4) schwannomas and meningiomas (P<0.001), 5) lymphomas from high-grade gliomas and metastases (P<0.001), and 6) typical meningiomas and atypical meningiomas (P<0.01).

CONCLUSION

rCBV ratios can help discriminate intracranial space-occupying lesions by demonstrating lesion vascularity. It is possible to discriminate between 1) high- and low-grade gliomas, 2) hemangioblastomas and other intracranial posterior fossa masses, 3) abscesses from high-grade gliomas and metastases, 4) schwannomas and meningiomas, 5) lymphomas and high-grade gliomas and metastases, and 6) typical and atypical meningiomas.

Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas

PURPOSE

To compare routine ROI analysis and three different histogram analyses in the grading of glial neoplasms. The hypothesis is that histogram methods can provide a robust and objective technique for quantifying perfusion data in brain gliomas. Current region-of-interest (ROI)-based methods for the analysis of dynamic susceptibility contrast perfusion magnetic resonance imaging (DSC MRI) data are operator-dependent.

MATERIALS AND METHODS

A total of 92 patients underwent conventional and DSC MRI. Multiple histogram metrics were obtained for cerebral blood flow (CBF), cerebral blood volume (CBV), and relative CBV (rCBV) maps using tumoral (T), peritumoral (P), and total tumoral (TT) analysis. Results were compared to histopathologic grades. Statistical analysis included Mann-Whitney (MW) tests, Spearman rank correlation coefficients, logistic regression, and McNemar tests.

RESULTS

The maximum value of rCBV (rCBV(max)) showed highly significant correlation with glioma grade (r = 0.734, P < 0.001). The strongest histogram correlations (P < 0.0001) occurred with rCBV(T) SD (r = 0.718), rCBV(P) SD(25) (r = 0.724) and rCBV(TT) SD(50) (r = 0.685). Multiple rCBV(T), rCBV(P), and rCBV(TT) histogram metrics showed significant correlations. CBF and CBV histogram metrics were less strongly correlated with glioma grade than rCBV histogram metrics.

CONCLUSION

Histogram analysis of perfusion MR provides prediction of glioma grade, with peritumoral metrics outperforming tumoral and total tumoral metrics. Further refinement may lead to automated methods for perfusion data analysis.

Spectroscopie 1H, perfusion, diffusion : quelle place pour ces techniques lors du diagnostic et du suivi des principales tumeurs cérébrales sus-tentorielles de l’adulte ?

INTRODUCTION

In a few years, magnetic resonance imaging (MRI) has evolved from a morphology-based examination to one that encompasses metabolism and function.

STATE OF ART

MRI is a well-established tool for the initial evaluation of brain tumors, but conventional MR sequences have some limitations. Conventional MRI is unable to distinguish high-grade glioma from metastasis and abscess, to define precisely the histopathological grade of gliomas, to determine exactly the limits of tumor extension, to characterize meningeal tumors. Differentiation of tumor recurrence from treatment-related changes may be difficult with standard MR imaging because the interpretation is essentially based on volume analysis.

PERSPECTIVES

1H Spectroscopy, diffusion and perfusion imaging become possible with the development of MR imagers and can be routinely performed in clinical settings. They give complementary information about tumor metabolism and vascularity and allow a better analysis of post-treatment modifications. Functional and metabolic explorations should be used to characterize brain tumors.

Diffusion Imaging: Insight to Cell Status and Cytoarchitecture

Diffusion MR imaging techniques are based on the molecular mobility of water, which is sensitive to interaction with intracellular elements, macromolecules, cell membranes, the density of cells, and microstructural organization. Disease processes that alter cell-water homeostasis, cell density, and cytoarchitecture affect water mobility and are quantifiable by diffusion MR imaging methodologies. Similarly, therapeutic intervention that alters these properties may be monitored for efficacy via diffusion MR imaging. This article outlines basic technical concepts and applications of diffusion imaging with an emphasis on oncology.

Correlation of Relative Permeability and Relative Cerebral Blood Volume in High-Grade Cerebral Neoplasms

OBJECTIVE

The purpose of this study was to correlate the degree of contrast enhancement on dynamic contrast-enhanced T1-weighted MRI and the relative cerebral blood volume (rCBV) values on T2*-weighted MRI in patients with high-grade brain neoplasms.

SUBJECTS AND METHODS

Ten patients with biopsy-proven high-grade gliomas underwent dynamic contrast-enhanced MRI using T1-weighted fast spoiled gradient-echo technique (TR/TE, 8.3/1.5) during i.v. infusion of 0.1 mmol/kg of MR contrast medium. This sequence was followed within 5 minutes by dynamic susceptibility contrast (DSC) imaging (1,500/80) during i.v. infusion of 0.2 mmol/kg of MR contrast medium. Dynamic contrast-enhanced analysis was performed using the maximum-signal-intensity algorithm, and DSC analysis was performed using the negative enhancement integral program. For each tumor, we performed two comparisons: first, the average dynamic contrast-enhanced and rCBV values within a region of interest drawn around the entire contrast-enhancing tumor on a single image through the center of the lesion and, second, the highest dynamic contrast-enhanced and highest rCBV values within each tumor. Statistical analyses of the first comparison were performed using Pearson's correlation coefficient, R2 correlation coefficient, and Spearman's rank correlation and for the second comparison using Kendall's tau correlation.

RESULTS

The mean signal intensity values ranged between 3.48 and 7.16 SDs above baseline values (mean, 4.89 SDs). The mean rCBV values ranged between 57.9% and 122.7% of the normal lentiform nucleus (mean, 76.6%). The Pearson's correlation coefficient was 0.867, the R2 correlation coefficient was 0.752, and the Spearman's rank correlation was 0.794 (p = 0.001). Dynamic contrast-enhanced values from the region of highest signal intensity ranged between 7.7 and 48.6 SDs above baseline values (mean, 17.3 SDs). The highest rCBV values ranged between 105% and 400% of the normal lentiform nucleus (mean, 292%). The correlation was estimated to be 0.7778 and was statistically significant at the 0.01 level of statistical significance (p = 0.0035).

CONCLUSION

We found a high correlation between degree of contrast enhancement on dynamic contrast-enhanced images and rCBV values in whole tumors and in regions having the highest degree of contrast enhancement in this small study. Our findings, which suggest that relative permeability and rCBV values may be correlated in high-grade glial neoplasms, deserve further study in a larger patient population.

Effect of brain tumor neovasculature defined by rCBV on BOLD fMRI activation volume in the primary motor cortex

We utilized blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) and MR perfusion imaging methods to study the influence of brain tumor neovascularity on the BOLD fMRI activation volume in the primary motor cortex (PMC). The results from 57 brain tumor cases demonstrated that, for grade IV gliomas only, decreases in the BOLD fMRI activation volumes within the ipsilateral PMC, when compared with that observed in the contralateral PMC, correlated with increases in the relative regional cerebral blood volume (rCBV) in the PMC. In addition, relative increases in the activation volumes, corresponding to decreases in the rCBV, exhibited a linear dependence on the distance between the grade IV glioma and PMC. These findings lend support to the hypothesis that decreases in the fMRI activation volumes adjacent to a GBM may, in part, be due to the increased contribution of aberrant tumor neovascularity, with the resultant de-coupling of blood flow from neuronal activity. The nature of the relationship between the resulting activation volumes and adjacent tumor characteristics is complex, but is found to be dependent on the tumor grade and type, as well as the distance of the tumor to the PMC.

Diffusion-weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response

Diffusion-weighted magnetic resonance (MR) imaging and perfusion MR imaging are advanced techniques that provide information not available from conventional MR imaging. In particular, these techniques have a number of applications with regard to characterization of tumors and assessment of tumor response to therapy. In this review, the authors describe the fundamental principles of diffusion-weighted and perfusion MR imaging and provide an overview of the ways in which these techniques are being used to characterize tumors by helping distinguish tumor types, assess tumor grade, and attempt to determine tumor margins. In addition, the role of these techniques for evaluating response to tumor therapy is outlined.

Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors

OBJECTIVE

Common contrast-enhancing malignant tumors of the brain are glioblastoma multiforme (GBMs), anaplastic astrocytomas (AAs), metastases, and lymphomas, all of which have sometimes similar conventional MRI findings. Our aim was to evaluate the role of perfusion MR imaging (PWI) and diffusion-weighted imaging (DWI) in the differentiation of these contrast-enhancing malignant cerebral tumors.

MATERIALS AND METHODS

Forty-eight patients with contrast-enhancing and histologically proven brain tumors, 14 AAs, 17 GBMs, nine metastases, and eight lymphomas, were included in the study. All patients have undergone routine MR examination where DWI and PWI were performed in the same session. DWI was performed with b values of 0, 500, and 1000 mm(2)/s. Minimum ADC values (ADC(min)) of each tumor was later calculated from ADC map images. PWI was applied using dynamic susceptibility contrast technique and maximum relative cerebral blood volume (rCBV(max)) was calculated from each tumor, given in ratio with contralateral normal white matter. Comparisons of ADC(min) and rCBV(max) values with the histological types of the enhancing tumors were made with a one-way analysis of variance and Bonferroni test. A P value less than 0.05 indicated a statistically significant difference.

RESULTS

The ADC(min) values (mean+/-S.D.) in GBMs, AAs, lymphomas, and metastases were 0.79+/-0.21 (x10(-3)mm(2)/s), 0.75+/-0.21 (x10(-3)mm(2)/s), 0.51+/-0.09 (x10(-3)mm(2)/s), and 0.68+/-0.11 (x10(-3)mm(2)/s), respectively. The difference in ADC(min) values were statistically significant between lymphomas and GBMs (P<0.05). It was also statistically significant between lymphomas and AAs (P<0.03). However, there were no differences between lymphomas and metastasis, and between GBMs, AAs, and metastasis. The rCBV(max) ratio (mean+/-S.D.) in GBMs were 6.33+/-2.03, whereas it was 3.66+/-1.79 in AAs, 2.33+/-0.68 in lymphomas, and 4.45+/-1.87 in metastases. These values were statistically different between GBMs and AAs (P<0.001), GBMs and lymphoma (P<0.0001). Although there seemed to be difference between GBMs and metastases, it was not statistically significant (P<0.083).

CONCLUSION

Combination of DWI and PWI, with ADC(min) and rCBV(max) calculations, may aid routine MR imaging in the differentiation of common cerebral contrast-enhancing malignant tumors.

Magnetic resonance brain perfusion imaging with voxel-specific arterial input functions

PURPOSE

To propose an automatic method for estimating voxel-specific arterial input functions (AIFs) in dynamic contrast brain perfusion imaging.

MATERIALS AND METHODS

Voxel-specific AIFs were estimated blindly using the theory of homomorphic transformations and complex cepstrum analysis. Wiener filtering was used in the subsequent deconvolution. The method was verified using simulated data and evaluated in 10 healthy adults.

RESULTS

Computer simulations accurately estimated differently shaped, normalized AIFs. Simple Wiener filtering resulted in underestimation of flow values. Preliminary in vivo results showed comparable cerebral flow value ratios between gray matter (GM) and white matter (WM) when using blindly estimated voxel-specific AIFs or a single manually selected AIF. Significant differences (P < or = 0.0125) in mean transit time (MTT) and time-to-peak (TTP) in GM compared to WM was seen with the new method.

CONCLUSION

Initial results suggest that the proposed method can replace the tedious and difficult task of manually selecting an AIF, while simultaneously providing better differentiation between time-dependent hemodynamic parameters.